The Bank of England Blasts The Threat To Capital Markets That Is High Frequency Trading

Tyler Durden's picture

Zero Hedge has been warnings about the scourge of High Frequency Trading long before most in the general public had even heard about the concept. Over the past 2 years, and culminating with the Flash Crash it became all too clear that HFT is nothing but a parasitic phenomenon which churns volume in stocks providing the best liquidity rebates, while pretending to be adding liquidity. Recently the best we can do is to provide glaring examples of HFT algos gone wrong in hopes that some regulator somewhere will finally take the long overdue step to establish a minimum bid/ask time delay and thus put virtually the entire HFT frontrunning math Ph.D. crew out of business. The latest development in the ongoing saga against these parasites comes from none other than the Bank of England's Andrew Haldane who prepared a speech to the International Economic Association Sixteenth World Congress in Beijing China, titled "The race to zero" which essentially recaps the hundreds if not thousands of posts we have written on the matter of risks posed by High Frequency Trading, and blasts the concept, as well as the toothless captured regulators who continue to exist in their zombie, porn-addicted state, and refuse to move one finger to finally end this next Flash Crash-in-waiting.

Some of the key excerpts:

The Flash Crash left market participants, regulators and academics agog. More than one year on, they remain agog. There has been no shortage of potential explanations. These are as varied as they are many: from fat fingers to fat tails; from block trades to blocked lines; from high-speed traders to low-level abuse. From this mixed bag, only one clear explanation emerges: that there is no clear explanation. To a first approximation, we remain unsure quite what caused the Flash Crash or whether it could recur.

That conclusion sits uneasily on the shoulders. Asset markets rely on accurate pricing of risk. And financial regulation relies on an accurate reading of markets. Whether trading assets or regulating exchanges, ignorance is rarely bliss. It is this uncertainty, rather than the Flash Crash itself, which makes this an issue of potential systemic importance.

In many respects, this uncertainty should come as no surprise. Driven by a potent cocktail of technology and regulation, trading in financial markets has evolved dramatically during the course of this century. Platforms for trading equities have proliferated and fragmented. And the speed limit for trading has gone through the roof. Technologists now believe the sky is the limit.

This rapidly-changing topology of trading raises some big questions for risk management. There are good reasons, theoretically and empirically, to believe that while this evolution in trading may have brought benefits such as a reduction in transaction costs, it may also have increased abnormalities in the distribution of risk and return in the financial system. Such abnormalities hallmarked the Flash Crash. This paper considers some of the evidence on these abnormalities and their impact on systemic risk.

His observations on broken market topology: another much discussed topic on ZH:

A diverse and distributed patchwork of exchanges and multilateral trading platforms has emerged in its place. These offer investors a range of execution characteristics, such as speed, cost and transparency, typically electronically. Equity market trading structures have fragmented. This has gone furthest in the US, where trading is now split across more than half a dozen exchanges, multilateral trading platforms and “dark pools” of anonymous trading (Charts 3 and 4). Having accounted for around 80% of trading volume in NYSE-listed securities in 2005, the trading share of the NYSE had fallen to around 24% by February 2011.

The average speed of order execution on the US NYSE has fallen from around 20 seconds a decade ago to around one second today. These days, the lexicon of financial markets is dominated by talk of High-Frequency Trading (HFT). It is not just talk. As recently as 2005, HFT accounted for less than a fifth of US equity market turnover by volume. Today, it accounts for between two-thirds and three-quarters.

The picture is similar, if less dramatic, in Europe. Since 2005, HFT has risen from a tiny share to represent over 35% of the equity market. In Asia and in emerging markets, it is growing fast from a lower base. What is true across countries is also true across markets. HFT is assuming an ever-increasing role in debt and foreign exchange markets. In some futures markets, it already accounts for almost half of turnover. In the space of a few years, HFT has risen from relative obscurity to absolute hegemony, at least in some markets.

On why HFT is nothing but a parasite to capital markets:

Taken together, this evidence suggests something important. Far from solving the liquidity problem in situations of stress, HFT firms appear to have added to it. And far from mitigating market stress, HFT appears to have amplified it. HFT liquidity, evident in sharply lower peacetime bid-ask spreads, may be illusory. In wartime, it disappears. This disappearing act, and the resulting liquidity void, is widely believed to have amplified the price discontinuities evident during the Flash Crash.13 HFT liquidity proved fickle under stress, as flood turned to drought.

Capital markets are now broken beyond a shadow of a doubt, first presented by Zero Hedge several months ago:

Recent studies point, however, to a changing pattern. Non-normal patterns in prices have begun to appear at much higher frequencies. A recent study by Smith (2010) suggests that, since around 2005, stock price returns have begun to exhibit fat-tailed persistence at 15 minute intervals. Given the timing, these non-normalities are attributed to the role of HFT in financial markets.

The measure of stock price abnormality used by Smith is the so-called “Hurst” coefficient. The Hurst coefficient is named after English civil engineer H E Hurst. It was constructed by plotting data on the irregular flooding patterns of the Nile delta over the period 622-1469 AD. Hurst found that flooding exhibited a persistent pattern. Large floods were not only frequent, but came in clumps. They had a long memory.

The Hurst coefficient summarises this behaviour in a single number. For example, a measured Hurst equal to 0.5 is consistent with the random walk model familiar from efficient markets theory. A Hurst coefficient above 0.5 implies fatter tails and longer memories. In his study, Smith finds that the Hurst coefficient among a selection of stocks has risen steadily above 0.5 since 2005. In other words, the advent of HFT has seen price dynamics mirror the fat-tailed persistence of the Nile flood plains.

Also discussed on Zero Hedge: self-similarity in HFT trading, leading to micromomentum bursts that have nothing to do with price discovery and everything to do with Chaos Theory:

To illustrate, Chart 11 plots the path of three simulated price series with Hurst coefficients of 0.5, 0.7 and 0.9. A higher Hurst coefficient radically alters the probability of sharp dislocations in prices. It also prolongs these dislocations. Prices become de-anchored and drift; their variance grows over time and is unbounded. If this long-memory property of prices is emerging at ever-higher frequencies, it might provide an important clue to how HFT affects systemic risk.

To see that, consider a sketch model of market-making. This builds on an analytical insight which is already more than 40 years old. It owes to the late Benoit Mandelbrot, French-American mathematician and between two measuring rods: clock time and volume time. While empirical studies typically used the first measuring rod (days, hours, seconds, milli-seconds), stock prices were better understood by using the second.

Mandelbrot’s explanation was relatively simple. If trading cannot occur within a given time window, price movements can only reflect random pieces of news – economic, financial, political. So, consistent with efficient market theory, price changes would be drawn from a normal distribution with a fat middle and thin tails when measured in clock time. They were a random walk.

But as soon as trading is possible within a period, this game changes. Strategic, interactive behaviour among participants enters the equation. Volumes come and go. Traders enter and exit. Algorithms die or adapt. Behaviour within that time interval may then no longer be random noise. Rather trading  volumes will exhibit persistence and fat tails. This will then be mirrored in prices. So when measured in clock time, prices changes will have thinner middles and fatter tails, just like a cauliflower, a coastline, or a cosmos.

Subsequent studies have shown that this clock time / volume time distinction helps explain equity price dynamics, especially at times of market stress. For example, Easley et al (2011) show that the distribution of price changes during the Flash Crash was highly non-normal in clock time, with fat tails and persistence. But in volume time, normal service – indeed, near-normality – resumed. This fractal lens can be used to explain why market liquidity can evaporate in situations of market stress, amplifying small events across time, assets and markets. Fractal geometry tells us that what might start off as a snowflake has the potential to snowball.

His observation on captured regulation:

Regulation has thin-sliced trading. And technology has thin-sliced time. Among traders, as among stocks on 6 May, there is a race to zero. Yet it is unclear that this race will have a winner. If it raises systemic risk, it is possible capital markets could be the loser. To avoid that, a redesign of mechanisms for securing capital market stability may be needed.

And the conclusion:

The Flash Crash was a near miss. It taught us something important, if uncomfortable, about our state of knowledge of modern financial markets. Not just that it was imperfect, but that these imperfections may magnify, sending systemic shockwaves. Technology allows us to thin-slice time. But thinner  technological slices may make for fatter market tails. Flash Crashes, like car crashes, may be more severe the greater the velocity.

Physical catastrophes alert us to the costs of ignoring these events, of normalising deviance. There is nothing normal about recent deviations in financial markets. The race to zero may have contributed to those abnormalities, adding liquidity during a monsoon and absorbing it during a drought. This fattens tail risk. Understanding and correcting those tail events is a systemic issue. It may call for new rules of the road for trading. Grit in the wheels, like grit on the roads, could help forestall the next crash.

Alas, nothing will happen. Until the next crash, when everyone will say nobody could have seen this happen. Nobody.

Much more in the full presentation, including many pretty charts (link)

BOE On HFT